# Graphical representation of hill number 0, 1 and 2 across a factor

Source:`R/plot_functions.R`

`hill_pq.Rd`

Note that this function use a sqrt of the read numbers in the linear model in order to correct for uneven sampling depth.

## Usage

```
hill_pq(
physeq,
variable,
color_fac = NA,
letters = FALSE,
add_points = FALSE,
add_info = TRUE,
one_plot = FALSE,
correction_for_sample_size = TRUE
)
```

## Arguments

- physeq
(required): a

`phyloseq-class`

object obtained using the`phyloseq`

package.- variable
(required): The variable to test. Must be present in the

`sam_data`

slot of the physeq object.- color_fac
(optional): The variable to color the barplot

- letters
(optional, default=FALSE): If set to TRUE, the plot show letters based on p-values for comparison. Use the

`multcompLetters`

function from the package multcompLetters. BROKEN for the moment. Note that na values in The variable param need to be removed (see examples) to use letters.- add_points
(logical): add jitter point on boxplot

- add_info
(logical, default TRUE) Do we add a subtitle with information about the number of samples per modality ?

- one_plot
(logical, default FALSE) If TRUE, return a unique plot with the four plot inside using the patchwork package. Note that if letters and one_plot are both TRUE, tuckey HSD results are discarded from the unique plot. In that case, use one_plot = FALSE to see the tuckey HSD results in the fourth plot of the resulting list.

- correction_for_sample_size
(logical, default TRUE) This function use a sqrt of the read numbers in the linear model in order to correct for uneven sampling depth.

## Value

Either an unique ggplot2 object (if one_plot is TRUE) or a list of 4 ggplot2 plot:

plot_Hill_0 : the boxplot of Hill number 0 (= species richness) against the variable

plot_Hill_1 : the boxplot of Hill number 1 (= Shannon index) against the variable

plot_Hill_2 : the boxplot of Hill number 2 (= Simpson index) against the variable

plot_tuckey : plot the result of the Tuckey HSD test

## Examples

```
p <- hill_pq(data_fungi, "Height")
#> Taxa are now in rows.
#> Cleaning suppress 0 taxa and 0 samples.
p_h1 <- p[[1]] + theme(legend.position = "none")
p_h2 <- p[[2]] + theme(legend.position = "none")
p_h3 <- p[[3]] + theme(legend.position = "none")
multiplot(plotlist = list(p_h1, p_h2, p_h3, p[[4]]), cols = 4)
# Artificially modify data_fungi to force alpha-diversity effect
data_fungi_modif <- clean_pq(subset_samples_pq(data_fungi, !is.na(data_fungi@sam_data$Height)))
#> Cleaning suppress 144 taxa and 0 samples.
data_fungi_modif@otu_table[data_fungi_modif@sam_data$Height == "High", ] <-
data_fungi_modif@otu_table[data_fungi_modif@sam_data$Height == "High", ] +
sample(c(rep(0, ntaxa(data_fungi_modif) / 2), rep(100, ntaxa(data_fungi_modif) / 2)))
p2 <- hill_pq(data_fungi_modif, "Height", letters = TRUE)
#> Taxa are now in rows.
#> Cleaning suppress 0 taxa and 0 samples.
#> Joining with `by = join_by(Height)`
#> Joining with `by = join_by(Height)`
#> Joining with `by = join_by(Height)`
```